I am continually amazed at how ignorant current tools are of the people actually driving the behaviors we are looking at.

Recently, a quiet buzz rose around an analysis of Twitter usage by a fellow running a pretty cool company called RJMetrics (yes, the name sucks. Yes, the initials of the founders are R and J). At Twitter Data Analysis: An Investor’s Perspective at TechCrunch, Robert J Moore examines Twitter usage in a couple of different ways. I wasn’t all that impressed with most of the analysis; it was pretty basic stuff.

But one of the ways I was most excited to see him highlight is the Cohort analysis. This is one of the most simple segmentations you can do: just take everyone who, say, did their first purchase in July 2009 (we’ll call this Time 0), and see what else they did over time (each month, say Time 1, Time 2, etc.). Do the same for everyone who did their first purchase in, say, Oct 2009. Then line everyone up on a graph so that everyone’s Time 0 is at the left, and then Time 1, etc. This lets you compare behaviors of clumps of people to see if their “lifecycle” is consistent.

But at the end of the day, beyond the value of this specific analysis, I admire that they are examining “people who” and then looking at “what they did”. So many analytic tools are stuck on “what they did” and forget the people part. So, you can get lists of most popular pages, but not who visited them. You can get lists of most often sold products… but can’t do anything to understand who bought them. And I don’t mean just getting a list of cookies; I mean actually having a group of people and comparing their behaviors to a different group of people.

Here are a couple of simple analyses; see if your web analytic tool can do them:

Of people who bought “loss leader” product, what else did they buy from you in the next 3 months from that purchase? Per person, how many different categories were the products in? Were any purchases done with promotions?

Of people who have bought “highly profitable” product, what was their original source? What kinds of things did they buy before the purchase in mind? What else did they buy after it?

Of people who have bought 3 or more times from you, what source drove them in originally? What is their purchase cadence? How many purchases had a promotion; how many were full price?

Here’s an easy one, one that most of the top tools in the market can’t do without bending over backwards: Have a running report that says how many times each person visited your site as a distribution: 80% of people visited once, 10% visited twice, 10% visited 3+. I allow any window you want to measure this: Month on month, all time, your choice. You won’t believe what I had to do to get this report out of one of my current tools.

As you can see, almost every question starts with a segment. (BTW, this is kind of unfair; even if I dropped the “people who” part, most tools can’t answer the questions above. That, too, is sad.) But the current tools have all sorts of limitations that prevent us from looking at “people who”:

Some only let you measure behaviors over the time of the segment. So, if I say “give me people who bought in June”, I can literally only analyze behaviors in June.

Some can’t merge segments. They can’t manipulate them at all, actually; you can’t get counts or overlaps

Some can’t keep updating segments; they exist only during the static time frame and you have to manually re-create them each month

Some let you make segments, but they can’t be applied to multiple reports; you just get whatever they give you.

Some use segments, but they are just filters; you can’t compare the segments vs. each other

This is really sad. I’ve had the luck over the past few weeks to use a bunch of different tools, and I am shocked at how poorly they let me examine my business. And yes, if you read What Web Analytics is Missing which I wrote over a year ago, you’d see that there has been 0 progress.

So, try doing a Cohort analysis of your business. Try looking at how groups differ, or are similar. And try to put people first in your analyses.

At the end of the day, I am trying to get people to buy things. The things don’t sell themselves… but looking at most web analytic offerings, that’s what they want me to think. After all, that’s all they are measuring.